Support-vector networks

Cited by (1,253)

Items 601–700 of 1,253

1. 4-Hz oscillations synchronize prefrontal–amygdala circuits during fear behavior
   Nikolaos Karalis, Cyril Déjean, Fabrice Chaudun, Suzana Khoder, Robert R. Rozeske, Hélène Wurtz, Sophie Bagur, Karim Benchenane, Anton Sirota, Julien Courtin et al. · 2016 · Nature Neuroscience
2. Applications of Support Vector Machines in Chemistry
   Ovidiu Ivanciuc · 2007 · Reviews in computational chemistry
3. Real-Time Detection and Recognition of Road Traffic Signs
   Jack Greenhalgh, Majid Mirmehdi · 2012 · IEEE Transactions on Intelligent Transportation Systems
4. Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression
   Mahdi Sharifzadeh, Alexandra Sikinioti-Lock, Nilay Shah · 2019 · Renewable and Sustainable Energy Reviews
5. A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review
   Tanveer Ahmad, Huanxin Chen, Yabin Guo, Jiangyu Wang · 2018 · Energy and Buildings
6. Deep learning in food category recognition
   Yudong Zhang, Lijia Deng, Hengde Zhu, Wei Wang, Zeyu Ren, Qinghua Zhou, Siyuan Lu, Shiting Sun, Ziquan Zhu, J. M. Górriz et al. · 2023 · Information Fusion
7. Data mining and machine learning in astronomy
   BALL, NICHOLAS M., BRUNNER, ROBERT J. · 2010 · International Journal of Modern Physics D
8. Machine Recognition of Music Emotion
   Yi‐Hsuan Yang, Homer H. Chen · 2012 · ACM Transactions on Intelligent Systems and Technology
9. A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis
   Huiling Chen, Bo Yang, Jie Liu, Dayou Liu · 2011 · Expert Systems with Applications
10. Incremental on-line learning: A review and comparison of state of the art algorithms
    Losing, Viktor, Hammer, Barbara, Wersing, Heiko · 2018 · Neurocomputing
11. A fast iterative nearest point algorithm for support vector machine classifier design
    S. Sathiya Keerthi, Shirish Shevade, Chiranjib Bhattacharyya, K. R. K. Murthy · 2000 · IEEE Transactions on Neural Networks
12. Text Categorization Based on Regularized Linear Classification Methods
    Tong Zhang, Frank J. Oles · 2001 · Information Retrieval
13. A review on prognostic techniques for non-stationary and non-linear rotating systems
    Man Shan Kan, Andy C.C. Tan, Joseph Mathew · 2015 · Mechanical Systems and Signal Processing
14. A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation
    Klass, Verena, Behm, Mårten, Lindbergh, Göran · 2014 · Journal of Power Sources
15. A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy
    Chih‐Hung Wu, Gwo‐Hshiung Tzeng, Yeong-Jia Goo, Wenchang Fang · 2006 · Expert Systems with Applications
16. Machine learning and integrative analysis of biomedical big data
    Mirza, Bilal, Wang, Wei, Wang, Jie, Choi, Howard, Chung, Neo Christopher, Ping, Peipei · 2019 · Genes
17. Classification of patterns of EEG synchronization for seizure prediction
    Mirowski, Piotr, Madhavan, Deepak, LeCun, Yann, Kuzniecky, Ruben · 2009 · Clinical Neurophysiology
18. Viewpoint: Human-in-the-loop Artificial Intelligence
    Zanzotto, Fabio Massimo · 2019 · Journal of Artificial Intelligence Research
19. Support vector machines for temporal classification of block design fMRI data
    Stephen M. LaConte, Stephen C. Strother, Vladimir Cherkassky, J ANDERSON, Xiaoping Philip Hu · 2005 · NeuroImage
20. Support Vector Machines
    Jaime Gòmez Sàenz de Tejada, Juan Seijas · 2007 · Springer eBooks
21. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection
    Jan Behmann, Anne‐Katrin Mahlein, Till Rumpf, Christoph Römer, Lutz Plümer · 2014 · Precision Agriculture
22. Artificial Intelligence in Drug Design
    Gerhard Heßler, Karl H. Baringhaus · 2018 · Molecules
23. FEMa: a finite element machine for fast learning
    Danilo Rodrigues Pereira, Marco Antônio Piteri, André Nunes de Souza, João Paulo Papa, Hojjat Adeli · 2019 · Neural Computing and Applications
24. Toward Supervised Anomaly Detection
    Nico Görnitz, M. Kloft, Konrad Rieck, Ulf Brefeld · 2013 · Journal of Artificial Intelligence Research
25. Fault detection, classification and location for transmission lines and distribution systems: a review on the methods
    Kunjin Chen, Caowei Huang, Jinliang He · 2016 · High Voltage
26. Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review
    Haonan Guo, Shubiao Wu, Yingjie Tian, Jun Zhang, Hongtao Liu · 2020 · Bioresource Technology
27. A geometric approach to Support Vector Machine (SVM) classification
    Michael Mavroforakis, Sergios Theodoridis · 2006 · IEEE Transactions on Neural Networks
28. Gearbox condition monitoring in wind turbines: A review
    Jack P. Salameh, Sébastien Cauët, Erik Etien, Anas Sakout, Laurent Rambault · 2018 · Mechanical Systems and Signal Processing
29. FaceTracer: A Search Engine for Large Collections of Images with Faces
    Neeraj Kumar, Peter N. Belhumeur, Shree K. Nayar · 2008 · Lecture notes in computer science
30. Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
    Feixiong Cheng, Zhongming Zhao · 2014 · Journal of the American Medical Informatics Association
31. A novel hybrid extreme learning machine–grey wolf optimizer (ELM-GWO) model to predict compressive strength of concrete with partial replacements for cement
    Mahdi Shariati, Mohammad Saeed Mafipour, Behzad Ghahremani, Fazel Azarhomayun, Masoud Ahmadi, Nguyen Thoi Trung, Ali Shariati · 2020 · Engineering With Computers
32. A survey on classification techniques for opinion mining and sentiment analysis
    Fatemeh Hemmatian, Mohammad Karim Sohrabi · 2017 · Artificial Intelligence Review
33. Deep learning for smart fish farming: applications, opportunities and challenges
    Xinting Yang, Song Zhang, Jintao Liu, Qinfeng Gao, Shuanglin Dong, Chao Zhou · 2020 · Reviews in Aquaculture
34. Fast support vector machine training and classification on graphics processors
    Catanzaro, Bryan, Sundaram, Narayanan, Keutzer, Kurt · 2008 · Proceedings of the 25th international conference on Machine learning - ICML '08
35. A deep convolutional neural network model for rapid prediction of fluvial flood inundation
    Syed Kabir, Sandhya Patidar, Xilin Xia, Qiuhua Liang, Jeffrey Charles Neal, Gareth Pender · 2020 · Journal of Hydrology
36. L2 Regularization for Learning Kernels
    Corinna Cortes, Mehryar Mohri, Afshin Rostamizadeh · 2012 · arXiv (Cornell University)
37. A Parallel Mixture of SVMs for Very Large Scale Problems
    Ronan Collobert, Samy Bengio A, Yoshua Bengio · 2002 · Neural Computation
38. Machine learning in major depression: from classification to treatment outcome prediction
    Gao, Shuang, Calhoun, Vince D., Sui, Jing · 2018 · CNS Neuroscience & Therapeutics
39. Correlations and Neuronal Population Information
    Adam Kohn, Ruben Coen-Cagli, Ingmar Kanitscheider, Alexandre Pouget · 2016 · Annual Review of Neuroscience
40. Few-Shot Learning approach for plant disease classification using images taken in the field
    David Argüeso, Artzai Picón, Unai Irusta, Alfonso Medela, Miguel G. San-Emeterio, Arantza Bereciartua, Aitor Álvarez-Gila · 2020 · Computers and Electronics in Agriculture
41. Interpretable bilinear attention network with domain adaptation improves drug–target prediction
    Peizhen Bai, Filip N. Miljković, Bino John, Haiping Lu · 2023 · Nature Machine Intelligence
42. Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
    Nicolas Papernot, Patrick Drew McDaniel · 2018 · arXiv (Cornell University)
43. Radiomics: a new application from established techniques
    Vishwa S. Parekh, Michael A. Jacobs · 2016 · Expert Review of Precision Medicine and Drug Development
44. Optimization problems for machine learning: A survey
    Claudio Gambella, Bissan Ghaddar, Joe Naoum‐Sawaya · 2020 · European Journal of Operational Research
45. Can SVM be used for automatic EEG detection of drowsiness during car driving?
    Mervyn V.M. Yeo, Xiaoping Li, Kaiquan Shen, Einar P. Wilder-Smith · 2008 · Safety Science
46. Advanced support vector machines and kernel methods
    V.David Sánchez A · 2003 · Neurocomputing
47. Learning on the border
    Şeyda Ertekin, Jian Long Huang, Léon Bottou, Clyde Lee Giles · 2007 · ACM International Conference on Information and Knowledge Management (CIKM)
48. Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis
    Thomas Walter Rauber, Francisco de Assis Boldt, Flávio Miguel Varejão · 2014 · IEEE Transactions on Industrial Electronics
49. What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach
    Khe Foon Hew, Xiang Yang Hu, Chen Qiao, Ying Tang · 2019 · Computers & Education
50. Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment
    Maher Ibrahim Sameen, Biswajeet Pradhan, Saro Lee · 2019 · CATENA
51. A Review of Face Recognition Technology
    Lixiang Li, Xiaohui Mu, Siying Li, Haipeng Peng · 2020 · IEEE Access
52. iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition
    Bin Liu, Longyun Fang, Ren Long, Xun Lan, Kuo‐Chen Chou · 2015 · Bioinformatics
53. Models for early prediction of at-risk students in a course using standards-based grading
    Farshid Marbouti, Heidi A. Diefes‐Dux, Krishna P.C. Madhavan · 2016 · Computers & Education
54. Fault location and detection techniques in power distribution systems with distributed generation: A review
    Sophi Shilpa Gururajapathy, Hazlie Mokhlis, Hazlee Azil Illias · 2017 · Renewable and Sustainable Energy Reviews
55. Authorship Attribution with Support Vector Machines
    Joachim Diederich, Jörg Kindermann, Edda Leopold, Gerhard Paaß · 2003 · Applied Intelligence
56. Building energy performance forecasting: A multiple linear regression approach
    Giuseppina Ciulla, Antonino D’Amico · 2019 · Applied Energy
57. A Survey of Learning Causality with Data: Problems and Methods
    Guo, Ruocheng, Cheng, Lu, Li, Jundong, Hahn, P. Richard, Liu, Huan · 2021 · ACM Computing Surveys
58. Phishing detection based Associative Classification data mining
    Neda Abdelhamid, Aladdin Ayesh, Fadi Thabtah · 2014 · Expert Systems with Applications
59. Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
    Dragomir Anguelov, Ben Taskar, Vassil Chatalbashev, Daphne Koller, Dhruv Gupta, Geremy Heitz, A.Y. Ng · 2005 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
60. A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model
    Jin Duan, Panagiotis G. Asteris, Hoang Nguyen, Xuan-Nam Bui, Hossein Moayedi · 2020 · Engineering With Computers
61. Machine learning for large-scale crop yield forecasting
    Dilli Paudel, Hendrik Boogaard, Allard J. W. de Wit, Sander J. C. Janssen, Sjoukje Osinga, Christos Pylianidis, Ioannis N. Athanasiadis · 2020 · Agricultural Systems
62. Intrusion Detection System for Healthcare Systems Using Medical and Network Data: A Comparison Study
    Anar A. Hady, Ali Ghubaish, Tara Salman, Devrim Ünal, Raj Kumar Jain · 2020 · IEEE Access
63. Tutorial: multivariate classification for vibrational spectroscopy in biological samples
    Camilo L. M. Morais, Kássio M. G. Lima, Maneesh N. Singh, Francis L. Martin · 2020 · Nature Protocols
64. Robustness and Regularization of Support Vector Machines
    Xu, Huan, Constantine Caramanis, Shie Mannor · 2008 · arXiv (Cornell University)
65. A bounded-error approach to piecewise affine system identification
    A. Bempora, Andrea Garulli, Simone Paoletti, Antonio Vicino · 2005 · IEEE Transactions on Automatic Control
66. Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
    Erno Mäkinen, Roope Raisamo · 2008 · IEEE Transactions on Pattern Analysis and Machine Intelligence
67. RetinaNet With Difference Channel Attention and Adaptively Spatial Feature Fusion for Steel Surface Defect Detection
    Xun Cheng, Jianbo Yu · 2020 · IEEE Transactions on Instrumentation and Measurement
68. A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring
    Matthew Lowe, Ruwen Qin, Xinwei Mao · 2022 · Water
69. Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier
    Masoumeh Zareapoor, Pourya Shamsolmoali · 2015 · Procedia Computer Science
70. A Machine Learning Approach to Ranging Error Mitigation for UWB Localization
    Henk Wymeersch, Stefano Maranò, Wesley M. Gifford, Moe Z. Win · 2012 · IEEE Transactions on Communications
71. dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
    Kede Ma, Wentao Liu, Tongliang Liu, Zhou Wang, Dacheng Tao · 2017 · IEEE Transactions on Image Processing
72. Multimodal sentiment analysis based on fusion methods: A survey
    Linan Zhu, Zhechao Zhu, Chenwei Zhang, Yifei Xu, Xiangjie Kong · 2023 · Information Fusion
73. Bayesian Optimization with Inequality Constraints
    Jacob R. Gardner, Matt J. Kusner, Zhixiang, Kilian Q. Weinberger, John Patrick Cunningham · 2014 · PolyPublie (École Polytechnique de Montréal)
74. Text-based CAPTCHA strengths and weaknesses
    Elie Bursztein, Matthieu Martin, John C. Mitchell · 2011 · ACM SIGSAC Conference on Computer and Communications Security (CCS)
75. Object Detection Using the Statistics of Parts
    Henry Schneiderman, Takeo Kanade A · 2004 · International Journal of Computer Vision
76. Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective
    Stefan Thomas, Matheus Thomas Kuśka, David Bohnenkamp, Anna Brugger, Elias Alisaac, Mirwaes Wahabzada, Jan Behmann, Anne‐Katrin Mahlein · 2017 · Journal of Plant Diseases and Protection
77. Neural network architectures and learning algorithms
    Bogdan M. Wilamowski · 2009 · IEEE Industrial Electronics Magazine
78. Randomness in neural networks: an overview
    Simone Scardapane, Dianhui Wang · 2017 · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
79. Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection
    Yuanlin Zhang, Yuan Yuan, Yachuang Feng, Xiaoqiang Lu · 2019 · IEEE Transactions on Geoscience and Remote Sensing
80. Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals
    Verma, Gyanendra K., Tiwary, Uma Shanker · 2014 · NeuroImage
81. A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination
    Farzaneh Sajedi Hosseini, Arash Malekian, Bahram Choubin, Omid Rahmati, Sabrina Cipullo, Frédéric Coulon, Biswajeet Pradhan · 2018 · The Science of The Total Environment
82. A Review on Conventional Machine Learning vs Deep Learning
    Nitin Kumar Chauhan, Krishna Pal Singh · 2018 · International Conference on Computing, Power and Communication Technologies (GUCON)
83. Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
    Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, David Novotný · 2021 · IEEE/CVF International Conference on Computer Vision (ICCV)
84. Boosting Sex Identification Performance
    Shumeet Baluja, Henry A. Rowley · 2006 · International Journal of Computer Vision
85. Predicting phishing websites based on self-structuring neural network
    Rami Mustafa A. Mohammad, Fadi Thabtah, Thomas Leo McCluskey · 2013 · Neural Computing and Applications
86. Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era
    Yankang Jing, Yuemin Bian, Ziheng Hu, Lirong Wang, Xiang-Qun Sean Xie · 2018 · The AAPS Journal
87. Long sequence time-series forecasting with deep learning: A survey
    Zonglei Chen, Minbo Ma, Tianrui Li, Hongjun Wang, Chongshou Li · 2023 · Information Fusion
88. Handbook of Mathematical Methods in Imaging
    Matti J. Lassas, Mikko Salo, Gunther A. Uhlmann · 2010 · Springer eBooks
89. Applications of Support Vector Machines for Pattern Recognition: A Survey
    Hyeran Byun, Seong–Whan Lee · 2002 · Lecture notes in computer science
90. A support vector machine to identify irrigated crop types using time-series Landsat NDVI data
    Baojuan Zheng, Soe Win Myint, Prasad S. Thenkabail, Rimjhim Aggarwal · 2014 · International Journal of Applied Earth Observation and Geoinformation
91. Advances and opportunities in machine learning for process data analytics
    Qin, S. Joe, Chiang, Leo H. · 2019 · Computers & Chemical Engineering
92. Practical Evasion of a Learning-Based Classifier: A Case Study
    Nedim Šrndić, Pavel Laskov · 2014 · IEEE Symposium on Security and Privacy (S&P)
93. Computer vision algorithms and hardware implementations: A survey
    Xin Feng, Youni Jiang, Xuejiao Yang, Ming Du, Xin Shane Li · 2019 · Integration
94. Feature-level fusion approaches based on multimodal EEG data for depression recognition
    Hanshu Cai, Zhidiao Qu, Zhe Li, Yi Zhang, Xiping Hu, Bin Hu · 2020 · Information Fusion
95. Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach
    Van Quan Tran, Viet Quoc Dang, Lanh Si Ho · 2022 · Construction and Building Materials
96. Local Receptive Fields Based Extreme Learning Machine
    Guang-Bin Huang, Zuo Bai, Liyanaarachchi Lekamalage Chamara Kasun, Chi‐Man Vong · 2015 · IEEE Computational Intelligence Magazine
97. Advanced, Analytic, Automated (AAA) Measurement of Engagement During Learning
    Sidney K. D’Mello, Ed Dieterle, Angela Lee Duckworth · 2017 · Educational Psychologist
98. Noise Reduction in Hyperspectral Imagery: Overview and Application
    Behnood Rasti, Paul Scheunders, Pedram Ghamisi, Giorgio Licciardi, Jocelyn Chanussot · 2018 · Remote Sensing
99. Granular ball computing classifiers for efficient, scalable and robust learning
    Shuyin Xia, Yunsheng Liu, Xin Ding, Guoyin Wang, Hong Zhi Yu, Yuoguo Luo · 2019 · Information Sciences
100. Machine Learning and Deep Learning in Energy Systems: A Review
     Mohammad Mahdi Forootan, Iman Larki, Rahim Zahedi, Abolfazl Ahmadi · 2022 · Sustainability